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ABSTRACT: BACKGROUND: Epidemiological studies suggest that excessive alcohol consumption is prevalent among adolescents and may have lasting neurobehavioral consequences. The use of animal models allows for the separation of the effects of adolescent ethanol (EtOH) exposure from genetic background and other environmental insults. In this study, the effects of moderate EtOH vapor exposure, during adolescence, on structural diffusion tensor imaging (DTI) and behavioral measures were evaluated in adulthood. METHODS: A total of 53 Wistar rats were received at postnatal day (PD) 21 and were randomly assigned to EtOH vapor (14 hours on/10 hours off/day) or air exposure for 35 days from PD 23 to 58 (average blood ethanol concentration: 169 mg%). Animals were received in 2 groups that were subsequently sacrificed at 2 time points following withdrawal from EtOH vapor: (i) at 72 days of age, 2 weeks following withdrawal or (ii) at day 128, 10 weeks following withdrawal. In the second group, behavior in the light/dark box and prepulse inhibition (PPI) of the startle was also evaluated. Fifteen animals in each group were scanned, postmortem, for structural DTI. RESULTS: There were no significant differences in body weight between EtOH and control animals. Volumetric data demonstrated that total brain, hippocampal, corpus callosum but not ventricular volume were significantly larger in the 128-day-sacrificed animals as compared to the 72 day animals. The hippocampus was smaller and the ventricles larger at 128 days as compared to 72 days, in the EtOH-exposed animals, leading to a significant group × time effect. EtOH-exposed animals sacrificed at 128 days also had diminished PPI, and more rears in the light box were significantly correlated with hippocampal size. CONCLUSIONS: These studies demonstrate that DTI volumetric measures of hippocampus are significantly impacted by age and peri-adolescent EtOH exposure and withdrawal in Wistar rats.
Alcoholism Clinical and Experimental Research 04/2013; · 3.34 Impact Factor
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Robert J. Lipinski,
Peter Hammond,
Shonagh K. O’Leary-Moore,
Jacob J. Ament,
Stephen J. Pecevich,
Yi Jiang,
Francois Budin,
Scott E. Parnell,
Michael Suttie,
Elizabeth A. Godin,
Joshua L. Everson,
Deborah B. Dehart, Ipek Oguz,
Hunter T. Holloway,
Martin A. Styner,
G. Allan Johnson,
Kathleen K. Sulik
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ABSTRACT: Prenatal ethanol exposure is the leading preventable cause of congenital mental disability. Whereas a diagnosis of fetal alcohol syndrome (FAS) requires identification of a specific pattern of craniofacial dysmorphology, most individuals with behavioral and neurological sequelae of heavy prenatal ethanol exposure do not exhibit these defining facial characteristics. Here, a novel integration of MRI and dense surface modeling-based shape analysis was applied to characterize concurrent face-brain phenotypes in C57Bl/6J fetuses exposed to ethanol on gestational day (GD)7 or GD8.5. The facial phenotype resulting from ethanol exposure depended upon stage of insult and was predictive of unique patterns of corresponding brain abnormalities. Ethanol exposure on GD7 produced a constellation of dysmorphic facial features characteristic of human FAS, including severe midfacial hypoplasia, shortening of the palpebral fissures, an elongated upper lip, and deficient philtrum. In contrast, ethanol exposure on GD8.5 caused mild midfacial hypoplasia and palpebral fissure shortening, a shortened upper lip, and a preserved philtrum. These distinct, stage-specific facial phenotypes were associated with unique volumetric and shape abnormalities of the septal region, pituitary, and olfactory bulbs. By demonstrating that early prenatal ethanol exposure can cause more than one temporally-specific pattern of defects, these findings illustrate the need for an expansion of current diagnostic criteria to better capture the full range of facial and brain dysmorphology in fetal alcohol spectrum disorders.
PLoS ONE 08/2012; 7(8). · 4.09 Impact Factor
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ABSTRACT: Diffusion tensor magnetic resonance imaging (DTI) has proven itself a powerful technique for clinical investigation of the neurobiological targets and mechanisms underlying developmental pathologies. The success of DTI in clinical studies has demonstrated its great potential for understanding translational animal models of clinical disorders, and preclinical animal researchers are beginning to embrace this new technology to study developmental pathologies. In animal models, genetics can be effectively controlled, drugs consistently administered, subject compliance ensured, and image acquisition times dramatically increased to reduce between-subject variability and improve image quality. When pairing these strengths with the many positive attributes of DTI, such as the ability to investigate microstructural brain organization and connectivity, it becomes possible to delve deeper into the study of both normal and abnormal development. The purpose of this review is to provide new preclinical investigators with an introductory source of information about the analysis of data resulting from small animal DTI studies to facilitate the translation of these studies to clinical data. In addition to an in-depth review of translational analysis techniques, we present a number of relevant clinical and animal studies using DTI to investigate developmental insults in order to further illustrate techniques and to highlight where small animal DTI could potentially provide a wealth of translational data to inform clinical researchers.
Developmental Neuroscience 05/2012; 34(1):5-19. · 3.63 Impact Factor
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ABSTRACT: Ethanol treatment on postnatal day seven (P7) causes robust brain cell death and is a model of late gestational alcohol exposure (Ikonomidou et al., 2000). To investigate the long-term effects of P7 ethanol treatment on adult brain, mice received either two doses of saline or ethanol on P7 (2.5 g/kg, s.c., 2 h apart) and were assessed as adults (P82) for brain volume (using postmortem MRI) and cellular architecture (using immunohistochemistry). Adult mice that received P7 ethanol had reduced MRI total brain volume (4%) with multiple brain regions being reduced in both males and females. Immunohistochemistry indicated reduced frontal cortical parvalbumin immunoreactive (PV + IR) interneurons (18-33%) and reduced Cux1+IR layer II pyramidal neurons (15%) in both sexes. Interestingly, markers of adult hippocampal neurogenesis differed between sexes, with only ethanol treated males showing increased doublecortin and Ki67 expression (52 and 57% respectively) in the dentate gyrus, consistent with increased neurogenesis compared to controls. These findings suggest that P7 ethanol treatment causes persistent reductions in adult brain volume and frontal cortical neurons in both males and females. Increased adult neurogenesis in males, but not females, is consistent with differential adaptive responses to P7 ethanol toxicity between the sexes. One day of ethanol exposure, e.g. P7, causes persistent adult brain dysmorphology.
Alcohol (Fayetteville, N.Y.) 05/2012; 46(6):603-12. · 2.41 Impact Factor
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ABSTRACT: The fate of pluripotent mesenchymal stem cells (MSC) is determined through integration of chemical, spatial, and physical signals. The suppression of MSC adipogenesis by mechanical stimuli, which requires Akt-induced inhibition of glycogen synthase kinase 3β (GSK3β) with β-catenin activation, can be enhanced by repetitive dosing within a single day. Here, we demonstrate that reapplication of cyclic strain within a 24-hour period leads to amplification of both Akt activation and its subsequent inhibition of GSK3β, such that total cycle number can be reduced while still inhibiting adipogenesis. Amplification of Akt signaling is facilitated by a dynamic restructuring of the cell in response to mechanical signals, as evidenced by a transient increase in focal adhesion (FA) number and increased RhoA activity. Preventing FA assembly or development of tension blocks activation of Akt by mechanical signals, but not by insulin. This indicates that the FA infrastructure is essential to the physical, but not necessarily the chemical, sensitivity, and responsiveness of the cell. Exploiting the transient nature of cytoskeletal remodeling may represent a process to enhance cell responsiveness to mechanical input and ultimately define the fate of MSCs with a minimal input.
Stem Cells 09/2011; 29(11):1829-36. · 7.78 Impact Factor
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ABSTRACT: Localized difference in the cortex is one of the most useful morphometric traits in human and animal brain studies. There are many tools and methods already developed to automatically measure and analyze cortical thickness for the human brain. However, these tools cannot be directly applied to rodent brains due to the different scales; even adult rodent brains are 50 to 100 times smaller than humans. This paper describes an algorithm for automatically measuring the cortical thickness of mouse and rat brains. The algorithm consists of three steps: segmentation, thickness measurement, and statistical analysis among experimental groups. The segmentation step provides the neocortex separation from other brain structures and thus is a preprocessing step for the thickness measurement. In the thickness measurement step, the thickness is computed by solving a Laplacian PDE and a transport equation. The Laplacian PDE first creates streamlines as an analogy of cortical columns; the transport equation computes the length of the streamlines. The result is stored as a thickness map over the neocortex surface. For the statistical analysis, it is important to sample thickness at corresponding points. This is achieved by the particle correspondence algorithm which minimizes entropy between dynamically moving sample points called particles. Since the computational cost of the correspondence algorithm may limit the number of corresponding points, we use thin-plate spline based interpolation to increase the number of corresponding sample points. As a driving application, we measured the thickness difference to assess the effects of adolescent intermittent ethanol exposure that persist into adulthood and performed t-test between the control and exposed rat groups. We found significantly differing regions in both hemispheres.
Proceedings - Society of Photo-Optical Instrumentation Engineers 03/2011; 7962:7962481-79624811.
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ABSTRACT: 3D Magnetic Resonance (MR) and Diffusion Tensor Imaging (DTI) have become important noninvasive tools for the study of animal models of brain development and neuropathologies. Fully automated analysis methods adapted to rodent scale for these images will allow high-throughput studies. A fundamental first step for most quantitative analysis algorithms is skull-stripping, which refers to the segmentation of the image into two tissue categories, brain and non-brain. In this manuscript, we present a fully automatic skull-stripping algorithm in an atlas-based manner. We also demonstrate how to either modify an external atlas or to build an atlas from the population itself to present a self-contained approach. We applied our method to three datasets of rat brain scans, at different ages (PND5, PND14 and adult), different study groups (control, ethanol exposed), as well as different image acquisition parameters. We validated our method by comparing the automated skull-strip results to manual delineations performed by our expert, which showed a discrepancy of less than a single voxel on average. We thus demonstrate that our algorithm can robustly and accurately perform the skull-stripping within one voxel of the manual delineation, and in a fraction of the time it takes a human expert.
Proceedings - Society of Photo-Optical Instrumentation Engineers 03/2011; 7962:7962251-7962257.
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ABSTRACT: The use of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) in animal models of neuropathology is of increasing interest to the neuroscience community. In this work, we present our approach to create optimal translational studies that include both animal and human neuroimaging data within the frameworks of a study of post-natal neuro-development in intra-uterine cocaine-exposure. We propose the use of non-invasive neuroimaging to study developmental brain structural and white matter pathway abnormalities via sMRI and DTI, as advanced MR imaging technology is readily available and automated image analysis methodology have recently been transferred from the human to animal imaging setting. For this purpose, we developed a synergistic, parallel approach to imaging and image analysis for the human and the rodent branch of our study. We propose an equivalent design in both the selection of the developmental assessment stage and the neuroimaging setup. This approach brings significant advantages to study neurobiological features of early brain development that are common to animals and humans but also preserve analysis capabilities only possible in animal research. This paper presents the main framework and individual methods for the proposed cross-species study design, as well as preliminary DTI cross-species comparative results in the intra-uterine cocaine-exposure study.
Frontiers in psychiatry / Frontiers Research Foundation. 01/2011; 2:53.
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ABSTRACT: High resolution diffusion tensor imaging (DTI) can provide important information on brain development, yet it is challenging in live neonatal rats due to the small size of neonatal brain and motion-sensitive nature of DTI. Imaging in live neonatal rats has clear advantages over fixed brain scans, as longitudinal and functional studies would be feasible to understand neuro-developmental abnormalities. In this study, we developed imaging strategies that can be used to obtain high resolution 3D DTI images in live neonatal rats at postnatal day 5 (PND5) and PND14, using only 3 h of imaging acquisition time. An optimized 3D DTI pulse sequence and appropriate animal setup to minimize physiological motion artifacts are the keys to successful high resolution 3D DTI imaging. Thus, a 3D rapid acquisition relaxation enhancement DTI sequence with twin navigator echoes was implemented to accelerate imaging acquisition time and minimize motion artifacts. It has been suggested that neonatal mammals possess a unique ability to tolerate mild-to-moderate hypothermia and hypoxia without long term impact. Thus, we additionally utilized this ability to minimize motion artifacts in magnetic resonance images by carefully suppressing the respiratory rate to around 15/min for PND5 and 30/min for PND14 using mild-to-moderate hypothermia. These imaging strategies have been successfully implemented to study how the effect of cocaine exposure in dams might affect brain development in their rat pups. Image quality resulting from this in vivo DTI study was comparable to ex vivo scans. fractional anisotropy values were also similar between the live and fixed brain scans. The capability of acquiring high quality in vivo DTI imaging offers a valuable opportunity to study many neurological disorders in brain development in an authentic living environment.
Frontiers in psychiatry / Frontiers Research Foundation. 01/2011; 2:54.
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Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
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ABSTRACT: The use of regional connectivity measurements derived from diffusion imaging datasets has become of considerable interest in the neuroimaging community in order to better understand cortical and subcortical white matter connectivity. Current connectivity assessment methods are based on streamline fiber tractography, usually applied in a Monte-Carlo fashion. In this work we present a novel, graph-based method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation method applied to sampled orientation distribution function (ODF), which can be computed directly from the original diffusion imaging data. We show early results of our method on synthetic and real datasets. The results illustrate the potential of our method towards subject-specific connectivity measurements that are performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for application in population studies of neuropathology, such as Autism, Huntington's Disease, Multiple Sclerosis or leukodystrophies. The proposed method is generic and could easily be applied to non-diffusion data as long as local directional data can be derived.
Proc SPIE 01/2011; 7962:79620S.
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ABSTRACT: This paper presents a novel method of optimizing point-based correspondence among populations of human cortical surfaces by combining structural cues with probabilistic connectivity maps. The proposed method establishes a tradeoff between an even sampling of the cortical surfaces (a low surface entropy) and the similarity of corresponding points across the population (a low ensemble entropy). The similarity metric, however, isn't constrained to be just spatial proximity, but uses local sulcal depth measurements as well as probabilistic connectivity maps, computed from DWI scans via a stochastic tractography algorithm, to enhance the correspondence definition. We propose a novel method for projecting this fiber connectivity information on the cortical surface, using a surface evolution technique. Our cortical correspondence method does not require a spherical parameterization. Experimental results are presented, showing improved correspondence quality demonstrated by a cortical thickness analysis, as compared to correspondence methods using spatial metrics as the sole correspondence criterion.
Information processing in medical imaging: proceedings of the ... conference 02/2009; 21:651-63.
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Information Processing in Medical Imaging, 21st International Conference, IPMI 2009, Williamsburg, VA, USA, July 5-10, 2009. Proceedings; 01/2009
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ABSTRACT: Establishing optimal correspondence across object populations is essential to statistical shape analysis. Minimizing the description length (MDL) is a popular method for finding correspondence. In this work, we extend the MDL method by incorporating various local curvature metrics. Using local curvature can improve performance by ensuring that corresponding points exhibit similar local geometric characteristics that can't always be captured by mere point locations. We illustrate results on a variety of anatomical structures. The MDL method with a combination of point locations and curvature outperforms all the other methods we analyzed, including traditional MDL and spherical harmonics (SPHARM) correspondence, when the analyzed object population exhibits complex structure. When the objects are of simple nature, however, there's no added benefit to using the local curvature. In our experiments, we did not observe a significant difference in the correspondence quality when different curvature metrics (e.g. principal curvatures, mean curvature, Gaussian curvature) were used.
Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, May 14-17, 2008; 01/2008
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ABSTRACT: Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology.The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T(2) two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives.
The insight journal. 01/2006;
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ABSTRACT: Statistical shape analysis has become of increasing interest to the neuroimaging community due to its potential to locate morphological changes. In this paper, we present the a novel combination of shape analysis and Diffusion Tensor Image (DTI) Tractography to the computation of a probabilistic, model based corpus callosum (CC) subdivision. The probabilistic subdivision is based on the distances of arc-length parameterized corpus callosum contour points to trans-callosal DTI fibers associated with an automatic lobe subdivision. Our proposed subdivision method is automatic and reproducible, Its results are more stable than the Witelson subdivision scheme or other commonly applied schemes based on the CC bounding box. We present the application of our subdivision method to a small scale study of regional CC area growth in healthy subjects from age 2 to 4 years.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2005; 8(Pt 2):765-72.
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Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part II; 01/2005
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ABSTRACT: Statistical shape analysis has become of increasing interest to the neuroimaging community due to its potential to locate
morphological changes. In this paper, we present the a novel combination of shape analysis and Diffusion Tensor Image (DTI)
Tractography to the computation of a probabilistic, model based corpus callosum (CC) subdivision. The probabilistic subdivision
is based on the distances of arc-length parameterized corpus callosum contour points to trans-callosal DTI fibers associated
with an automatic lobe subdivision. Our proposed subdivision method is automatic and reproducible. Its results are more stable
than the Witelson subdivision scheme or other commonly applied schemes based on the CC bounding box. We present the application
of our subdivision method to a small scale study of regional CC area growth in healthy subjects from age 2 to 4 years.
01/1970: pages 765-772;
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ABSTRACT: Many statistical shape analysis methods produce various types of data about the analyzed surfaces, such as p-value maps, distance maps, 3D difference vectors and local covariance matrices. This data is often too large and thus difficult to be properly evaluated on a qualitative basis. A visual representation of this data strongly simplifies qualitative evaluation by humans and thus greatly enhances the value of the statistical results. In this paper we present a new tool for visualizing various datasets on surfaces represented as triangle meshes. Our tool, KWMeshVisu, is implemented using the Insight Toolkit ITK, www.itk.org, the Visualization Toolkit VTK, www.vtk.org, and the KWWidgets user interface toolkit, www.kwwidgets.org. The source code for KWMeshVisu, as well as input data used to generate the images in this paper, is provided with this document. NIH Roadmap for Medical Research Grant U54 EB005149-01 UNC Neurodevelopmental Disorders Research Center HD 03110 NIH NIBIB grant P01 EB002779
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ABSTRACT: Statistical Shape Models are a popular method for segmenting three-dimensional medical images. To obtain the required landmark correspondences, various automatic approaches have been proposed. In this work, we present an improved version of minimizing the description length (MDL) of the model. To initialize the algorithm, we describe a method to distribute landmarks on the training shapes using a conformal parameterization function. Then, we introduce a novel procedure to modify landmark positions locally without disturbing established correspondences. We employ a gradient descent optimization to minimize the MDL cost function, speeding up automatic model building by several orders of magnitude when compared to the original MDL approach. The necessary gradient information is estimated from a singular value decomposition, a more accurate technique to calculate the PCA than the commonly used eigendecomposition of the covariance matrix. In this work, we first present a basic version where spatial locations are used in the MDL cost function; next, we introduce an extended version where any combination of features can be used as a metric. As an example application, we present results based on local curvature measurements. Finally, we present results for synthetic and real-world datasets demonstrating the efficiency of our procedures and give details about the implementation using the Insight Toolkit (ITK). NIH Roadmap for Medical Research Grant U54 EB005149-01 UNC Neurodevelopmental Disorders Research Center HD 03110 NIH NIBIB grant P01 EB002779.
